Combustion is currently the main method to produce energy in the world energy matrix. Through the burning of oil, coal and natural gas, mankind supplies its energy needs while migrating in a balanced way to renewable energy sources. From this context comes the need for efficient control of combustion, a key process in reducing the consequences of the greenhouse effect, and hence an accurate measurement of CO2 and O2 emissions in combustion processes. Due to the hostile process conditions such as high temperature, difficult access to the controlled area and the risk of explosion, the use of sensors on the flame is often impractical and image monitoring has proven to be a convenient alternative. In this paper, an inference device based on artificial neural networks that uses monochromatic flame images captured by a CCD (chargedcoupled device) camera is designed to estimate CO2 and O2 emissions in a natural gas furnace. The proposed method seems promising: it estimates without delay, unlike traditional gas analyzers, and with good accuracy on the tested equivalence ratios (root mean square error below 2%)
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